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    "# Backtesting\n",
    "\n",
    "In time series forecasting, the process of *backtesting* consists of **evaluating the performance of a predictive model by applying it retrospectively to historical data**. Therefore, it is a special type of cross-validation applied to the previous period(s).\n",
    "\n",
    "The purpose of backtesting is to **evaluate the accuracy and effectiveness of a model** and identify any potential issues or areas of improvement. By testing the model on historical data, one can assess how well it performs on data that it has not seen before. This is an important step in the modeling process, as it helps to ensure that the model is robust and reliable.\n",
    "\n",
    "Backtesting can be done using a **variety of techniques**, such as simple train-test splits or more sophisticated methods like rolling windows or expanding windows. The choice of method depends on the specific needs of the analysis and the characteristics of the time series data.\n",
    "\n",
    "Overall, backtesting is an **essential step** in the development of a time series forecasting model. By rigorously testing the model on historical data, one can improve its accuracy and ensure that it is effective at predicting future values of the time series.\n",
    "\n",
    "## Backtesting without refit\n",
    "\n",
    "Backtesting without refit is a strategy where the **model is trained only once** and used sequentially without updating it, following the temporal order of the data. This approach is advantageous as it is much faster than other methods that require retraining the model each time. However, the model may lose its predictive power over time as it does not incorporate the latest information available.\n",
    "\n",
    "<p style=\"text-align: center\">\n",
    "    <img src=\"../img/diagram-backtesting-no-refit.png\" style=\"width: 500px;\">\n",
    "</p>\n",
    "\n",
    "<p style=\"text-align: center\">\n",
    "    <img src=\"../img/time-series-backtesting-forecasting-no-refit.gif\" style=\"width: 650px;\">\n",
    "    <br>\n",
    "    <font size=\"2.5\"> <i>Backtesting without refit</i></font>\n",
    "</p>\n",
    "\n",
    "*Note: The argument needed to achieve this configuration is `refit=False`.*\n",
    "\n",
    "## Backtesting with refit and increasing training size\n",
    "\n",
    "In this strategy, the **model is trained before making predictions each time**, and all available data up to that point is used in the training process. This differs from standard cross-validation, where the data is randomly distributed between training and validation sets.\n",
    "\n",
    "Instead of randomizing the data, this backtesting sequentially increases the size of the training set while maintaining the temporal order of the data. By doing this, the model can be tested on progressively larger amounts of historical data, providing a more accurate assessment of its predictive capabilities.\n",
    "\n",
    "<p style=\"text-align: center\">\n",
    "    <img src=\"../img/diagram-backtesting-refit.png\" style=\"width: 500px;\">\n",
    "</p>\n",
    "\n",
    "<p style=\"text-align: center\">\n",
    "    <img src=\"../img/time-series-backtesting-forecasting-with-refit.gif\" style=\"width: 650px;\">\n",
    "    <br>\n",
    "    <font size=\"2.5\"> <i>Backtesting with refit and increasing training size (fixed origin)</i></font>\n",
    "</p>\n",
    "\n",
    "*Note: The arguments needed to achieve this configuration are `refit=True` and `fixed_train_size=False`.*\n",
    "\n",
    "## Backtesting with refit and fixed training size\n",
    "\n",
    "In this approach, the **model is trained using a fixed window of past observations**, and the testing is performed on a rolling basis, where the training window is moved forward in time. The size of the training window is kept constant, allowing for the model to be tested on different sections of the data. This technique is particularly useful when there is a limited amount of data available, or when the data is non-stationary, and the model's performance may vary over time. Is also known as time series cross-validation or walk-forward validation.\n",
    "\n",
    "<p style=\"text-align: center\">\n",
    "    <img src=\"../img/diagram-backtesting-refit-fixed-train-size.png\" style=\"width: 500px;\">\n",
    "</p>\n",
    "\n",
    "<p style=\"text-align: center\">\n",
    "    <img src=\"../img/time-series-backtesting-forecasting-refit-fixed-train-size.gif\" style=\"width: 650px;\">\n",
    "    <br>\n",
    "    <font size=\"2.5\"> <i>Backtesting with refit and fixed training size (rolling origin)</i></font>\n",
    "</p>\n",
    "\n",
    "*Note: The arguments needed to achieve this configuration are `refit=True` and `fixed_train_size=True`.*\n",
    "\n",
    "## Backtesting with intermittent refit\n",
    "\n",
    "The model is **retrained every *n* iterations**, which is useful when the retraining frequency differs from the prediction frequency. For example, a model may be updated weekly but used to generate daily forecasts.\n",
    "\n",
    "It can be applied with either a fixed or rolling origin, offering flexibility in how the model adapts to new data.\n",
    "\n",
    "<div class=\"admonition note\" name=\"html-admonition\" style=\"background: rgba(0,191,191,.1); padding-top: 0px; padding-bottom: 6px; border-radius: 8px; border-left: 8px solid #00bfa5; border-color: #00bfa5; padding-left: 10px; padding-right: 10px;\">\n",
    "\n",
    "<p class=\"title\">\n",
    "    <i style=\"font-size: 18px; color:#00bfa5;\"></i>\n",
    "    <b style=\"color: #00bfa5;\">&#128161 Tip</b>\n",
    "</p>\n",
    "\n",
    "This strategy usually achieves a good balance between the computational cost of retraining and avoiding model degradation.\n",
    "\n",
    "</div>\n",
    "\n",
    "<p style=\"text-align: center\">\n",
    "    <img src=\"../img/time-series-backtesting-forecasting-intermittent-refit.gif\" style=\"width: 650px;\">\n",
    "    <br>\n",
    "    <font size=\"2.5\"> <i>Backtesting with intermittent refit</i></font>\n",
    "</p>\n",
    "\n",
    "*Note: The argument needed to achieve this configuration is `refit=n`, where $n$ is an integer. This configuration also allows the use of `fixed_train_size`.*\n",
    "\n",
    "## Backtesting including gap\n",
    "\n",
    "This approach introduces a **time gap between the training and test sets**, replicating a scenario where predictions cannot be made immediately after the end of the training data.\n",
    "\n",
    "For example, consider the goal of predicting the 24 hours of day D+1, but the predictions need to be made at 11:00 to allow sufficient flexibility. At 11:00 on day D, the task is to forecast hours [12 - 23] of the same day and hours [0 - 23] of day D+1. Thus, a total of 36 hours into the future must be predicted, with only the last 24 hours to be stored.\n",
    "\n",
    "<p style=\"text-align: center\">\n",
    "    <img src=\"../img/time-series-backtesting-forecasting-with-gap.gif\" style=\"width: 650px;\">\n",
    "    <br>\n",
    "    <font size=\"2.5\"> <i>Backtesting with refit and gap</i></font>\n",
    "</p>\n",
    "\n",
    "*Note: After setting the desired values for `refit` and `fixed_train_size`. The argument needed to achieve this configuration is `gap=n`, where $n$ is an integer.*\n",
    "\n",
    "## Backtesting with fold stride\n",
    "\n",
    "This method involves **controlling how far the test set moves forward between folds**. For example, you might want to forecast the next 30 days but generate a new forecast every 7 days. In this setup, each test window is 30 days long (`steps=30`), while the stride between folds is one week (`fold_stride=7`). This means forecasts overlap, and the same observations are predicted multiple times, which provides a richer view of model consistency and stability across different forecast origins.\n",
    "\n",
    "<p style=\"text-align: center\">\n",
    "    <img src=\"../img/time-series-backtesting-forecasting-fold-stride.gif\" style=\"width: 650px;\">\n",
    "    <br>\n",
    "    <font size=\"2.5\"> <i>Backtesting with fold stride</i></font>\n",
    "</p>\n",
    "\n",
    "*Note: The argument needed to achieve this configuration is `fold_stride=n`, where $n$ is an integer.*\n",
    "\n",
    "Depending on the relation between `steps` and `fold_stride`, the output may include repeated indexes (if `fold_stride < steps`) or gaps (if `fold_stride > steps`).\n",
    "\n",
    "## Skip folds\n",
    "\n",
    "All of the above backtesting strategies can be combined with the option **to skip a certain number of folds** by setting the `skip_folds` argument. Since the model predicts fewer points in time, the computational cost is reduced and the backtesting process is faster. This is particularly useful when one is interested in an approximate estimate of the model's performance, but does not require an exact evaluation, for example, when searching for hyperparameters. If `skip_folds` is an integer, every 'skip_folds'-th is returned. If `skip_folds` is a list, the folds in the list are skipped. For example, if `skip_folds = 3`, and there are 10 folds, the returned folds will be [0, 3, 6, 9]. If `skip_folds` is a list [1, 2, 3], the returned folds will be [0, 4, 5, 6, 7, 8, 9].\n",
    "\n",
    "## Which strategy should I use?\n",
    "\n",
    "To ensure an accurate evaluation of your model and gain confidence in its predictive performance on new data, it is critical to employ an **appropriate backtesting strategy**.\n",
    "\n",
    "The choice depends on **factors** such as the use case, available computing resources, and the time interval between predictions. These will determine both the **prediction horizon** and the **refit strategy**.\n",
    "\n",
    "+ **Prediction horizon** (`steps`): Suppose you need to forecast the users of an app every Monday for the whole week. In this case, each iteration of backtesting would be a seven-step prediction, representing the seven days of the week.\n",
    "  \n",
    "+ **Refit strategy** (`refit`): Continuing with the example above, at the end of each week you must decide whether to update the model. Retraining with additional data can improve accuracy but increases time and computational cost. A reasonable approach is to retrain only when the error metric shows a consistent upward trend. This behavior can be effectively simulated with the backtesting framework.\n",
    "\n",
    "As an illustration, backtesting is performed using the data from this [skforecast example](https://www.cienciadedatos.net/documentos/py29-forecasting-electricity-power-demand-python.html). The same backtest is run with `steps=24` (predicting 24 hours ahead) and different `refit` strategies: `False` (no refit between predictions), re-fitting every `30` days, every `14` days, every `7` days, and `True` (re-fitting after every prediction). Notice that the significant increase in computation time does not necessarily correspond to lower error.\n",
    "\n",
    "| refit value | execution time (s) | metric |\n",
    "|------------:|-------------------:|-------:|\n",
    "|       False |                1.4 |  262.5 |\n",
    "|          30 |                4.0 |  263.4 |\n",
    "|          14 |                6.3 |  262.5 |\n",
    "|           7 |               11.1 |  261.4 |\n",
    "|        True |               69.1 |  258.3 |"
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    "<div class=\"admonition note\" name=\"html-admonition\" style=\"background: rgba(0,191,191,.1); padding-top: 0px; padding-bottom: 6px; border-radius: 8px; border-left: 8px solid #00bfa5; border-color: #00bfa5; padding-left: 10px; padding-right: 10px;\">\n",
    "\n",
    "<p class=\"title\">\n",
    "    <i style=\"font-size: 18px; color:#00bfa5;\"></i>\n",
    "    <b style=\"color: #00bfa5;\">&#128161 Tip</b>\n",
    "</p>\n",
    "\n",
    "<b>Best practice:</b> The reliability of backtesting results depends on how well the setup mirrors the real-world use case. The closer the backtesting process is to the actual conditions under which the model will operate — including prediction horizon, refit frequency, and data availability — the more trustworthy the estimated performance metrics will be.\n",
    "\n",
    "</div>"
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   "source": [
    "<div class=\"admonition note\" name=\"html-admonition\" style=\"background: rgba(0,184,212,.1); padding-top: 0px; padding-bottom: 6px; border-radius: 8px; border-left: 8px solid #00b8d4; border-color: #00b8d4; padding-left: 10px; padding-right: 10px;\">\n",
    "\n",
    "<p class=\"title\">\n",
    "    <i style=\"font-size: 18px; color:#00b8d4;\"></i>\n",
    "    <b style=\"color: #00b8d4;\">&#9998 Note</b>\n",
    "</p>\n",
    "\n",
    "All backtesting and grid search functions have been extended to include the <code>n_jobs</code> argument, allowing multi-process parallelization for improved performance. This applies to all functions of the different <code>model_selection</code> modules. \n",
    "\n",
    "The benefits of parallelization depend on several factors, including the estimator used, the number of fits to be performed, and the volume of data involved. When the <code>n_jobs</code> parameter is set to <code>'auto'</code>, the level of parallelization is automatically selected based on heuristic rules that aim to choose the best option for each scenario.\n",
    "\n",
    "For a more detailed look at parallelization, visit <a href=\"../faq/parallelization-skforecast.html\">Parallelization in skforecast</a>.\n",
    "\n",
    "</div>"
   ]
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   "source": [
    "## Libraries and data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b5cf6dad",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Libraries\n",
    "# ==============================================================================\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.linear_model import Ridge\n",
    "from lightgbm import LGBMRegressor\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from skforecast.datasets import fetch_dataset\n",
    "from skforecast.preprocessing import RollingFeatures\n",
    "from skforecast.recursive import ForecasterRecursive\n",
    "from skforecast.model_selection import TimeSeriesFold\n",
    "from skforecast.model_selection import backtesting_forecaster\n",
    "from skforecast.plot import (\n",
    "    set_dark_theme, \n",
    "    plot_prediction_intervals, \n",
    "    backtesting_gif_creator\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0520b926",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-04T18:35:16.660907Z",
     "start_time": "2022-03-04T18:35:16.553084Z"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">╭────────────────────────────────────── <span style=\"font-weight: bold\">h2o</span> ───────────────────────────────────────╮\n",
       "│ <span style=\"font-weight: bold\">Description:</span>                                                                     │\n",
       "│ Monthly expenditure ($AUD) on corticosteroid drugs that the Australian health    │\n",
       "│ system had between 1991 and 2008.                                                │\n",
       "│                                                                                  │\n",
       "│ <span style=\"font-weight: bold\">Source:</span>                                                                          │\n",
       "│ Hyndman R (2023). fpp3: Data for Forecasting: Principles and Practice(3rd        │\n",
       "│ Edition). http://pkg.robjhyndman.com/fpp3package/,https://github.com/robjhyndman │\n",
       "│ /fpp3package, http://OTexts.com/fpp3.                                            │\n",
       "│                                                                                  │\n",
       "│ <span style=\"font-weight: bold\">URL:</span>                                                                             │\n",
       "│ https://raw.githubusercontent.com/skforecast/skforecast-                         │\n",
       "│ datasets/main/data/h2o.csv                                                       │\n",
       "│                                                                                  │\n",
       "│ <span style=\"font-weight: bold\">Shape:</span> 204 rows x 2 columns                                                      │\n",
       "╰──────────────────────────────────────────────────────────────────────────────────╯\n",
       "</pre>\n"
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      "text/plain": [
       "╭────────────────────────────────────── \u001b[1mh2o\u001b[0m ───────────────────────────────────────╮\n",
       "│ \u001b[1mDescription:\u001b[0m                                                                     │\n",
       "│ Monthly expenditure ($AUD) on corticosteroid drugs that the Australian health    │\n",
       "│ system had between 1991 and 2008.                                                │\n",
       "│                                                                                  │\n",
       "│ \u001b[1mSource:\u001b[0m                                                                          │\n",
       "│ Hyndman R (2023). fpp3: Data for Forecasting: Principles and Practice(3rd        │\n",
       "│ Edition). http://pkg.robjhyndman.com/fpp3package/,https://github.com/robjhyndman │\n",
       "│ /fpp3package, http://OTexts.com/fpp3.                                            │\n",
       "│                                                                                  │\n",
       "│ \u001b[1mURL:\u001b[0m                                                                             │\n",
       "│ https://raw.githubusercontent.com/skforecast/skforecast-                         │\n",
       "│ datasets/main/data/h2o.csv                                                       │\n",
       "│                                                                                  │\n",
       "│ \u001b[1mShape:\u001b[0m 204 rows x 2 columns                                                      │\n",
       "╰──────────────────────────────────────────────────────────────────────────────────╯\n"
      ]
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     "output_type": "display_data"
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       "      <th></th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
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       "    <tr>\n",
       "      <th>1991-07-01</th>\n",
       "      <td>0.429795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991-08-01</th>\n",
       "      <td>0.400906</td>\n",
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       "      <th>1991-09-01</th>\n",
       "      <td>0.432159</td>\n",
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       "                   y\n",
       "datetime            \n",
       "1991-07-01  0.429795\n",
       "1991-08-01  0.400906\n",
       "1991-09-01  0.432159"
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   ],
   "source": [
    "# Download data\n",
    "# ==============================================================================\n",
    "data = fetch_dataset(\n",
    "    name=\"h2o\", raw=True, kwargs_read_csv={\"names\": [\"y\", \"datetime\"], \"header\": 0}\n",
    ")\n",
    "\n",
    "# Data preprocessing\n",
    "# ==============================================================================\n",
    "data['datetime'] = pd.to_datetime(data['datetime'], format='%Y-%m-%d')\n",
    "data = data.set_index('datetime')\n",
    "data = data.asfreq('MS')\n",
    "data = data[['y']]\n",
    "data = data.sort_index()\n",
    "data.head(3)"
   ]
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     "text": [
      "Train dates      : 1991-07-01 00:00:00 --- 2002-01-01 00:00:00  (n=127)\n",
      "Validation dates : 2002-02-01 00:00:00 --- 2008-06-01 00:00:00  (n=77)\n"
     ]
    },
    {
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",
      "text/plain": [
       "<Figure size 600x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Train-validation dates\n",
    "# ==============================================================================\n",
    "end_train = '2002-01-01 23:59:00'\n",
    "\n",
    "print(\n",
    "    f\"Train dates      : {data.index.min()} --- {data.loc[:end_train].index.max()}\"\n",
    "    f\"  (n={len(data.loc[:end_train])})\"\n",
    ")\n",
    "print(\n",
    "    f\"Validation dates : {data.loc[end_train:].index.min()} --- {data.index.max()}\"\n",
    "    f\"  (n={len(data.loc[end_train:])})\"\n",
    ")\n",
    "\n",
    "# Plot\n",
    "# ==============================================================================\n",
    "set_dark_theme()\n",
    "fig, ax = plt.subplots(figsize=(6, 3))\n",
    "data.loc[:end_train, 'y'].plot(ax=ax, label='train')\n",
    "data.loc[end_train:, 'y'].plot(ax=ax, label='validation')\n",
    "ax.legend()\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c6e713c",
   "metadata": {},
   "source": [
    "## TimeSeriesFold\n",
    "\n",
    "The [`TimeSeriesFold`](../api/model_selection.html#skforecast.model_selection._split.TimeSeriesFold) class is designed to generate the partitions used in the backtesting process to train and evaluate the model. By leveraging its various arguments, it provides extensive flexibility, enabling the simulation of scenarios such as refit, no refit, rolling origin, and others. The `split` method returns the index positions of the time series corresponding to each partition. When `as_pandas=True` is specified, the output is a DataFrame with detailed information, including descriptive column names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "541ca35d",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
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       "      <th>fit_forecaster</th>\n",
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       "      <td>157</td>\n",
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       "      <td>167</td>\n",
       "      <td>True</td>\n",
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       "      <th>4</th>\n",
       "      <td>4</td>\n",
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       "      <td>177</td>\n",
       "      <td>187</td>\n",
       "      <td>187</td>\n",
       "      <td>197</td>\n",
       "      <td>187</td>\n",
       "      <td>197</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>197</td>\n",
       "      <td>187</td>\n",
       "      <td>197</td>\n",
       "      <td>197</td>\n",
       "      <td>204</td>\n",
       "      <td>197</td>\n",
       "      <td>204</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fold  train_start  train_end  last_window_start  last_window_end  \\\n",
       "0     0            0        127                117              127   \n",
       "1     1            0        137                127              137   \n",
       "2     2            0        147                137              147   \n",
       "3     3            0        157                147              157   \n",
       "4     4            0        167                157              167   \n",
       "5     5            0        177                167              177   \n",
       "6     6            0        187                177              187   \n",
       "7     7            0        197                187              197   \n",
       "\n",
       "   test_start  test_end  test_start_with_gap  test_end_with_gap  \\\n",
       "0         127       137                  127                137   \n",
       "1         137       147                  137                147   \n",
       "2         147       157                  147                157   \n",
       "3         157       167                  157                167   \n",
       "4         167       177                  167                177   \n",
       "5         177       187                  177                187   \n",
       "6         187       197                  187                197   \n",
       "7         197       204                  197                204   \n",
       "\n",
       "   fit_forecaster  \n",
       "0            True  \n",
       "1            True  \n",
       "2            True  \n",
       "3            True  \n",
       "4            True  \n",
       "5            True  \n",
       "6            True  \n",
       "7            True  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create TimeSeriesFold\n",
    "# ==============================================================================\n",
    "cv = TimeSeriesFold(\n",
    "         steps                 = 10,\n",
    "         initial_train_size    = len(data.loc[:end_train]),\n",
    "         fold_stride           = None,\n",
    "         window_size           = 10,\n",
    "         refit                 = True,\n",
    "         fixed_train_size      = False,\n",
    "         gap                   = 0,\n",
    "         allow_incomplete_fold = True,\n",
    "         verbose               = False\n",
    "     )\n",
    "\n",
    "cv.split(X=data, as_pandas=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99972823",
   "metadata": {},
   "source": [
    "It is posible to use a datetime (string compatible with pandas or a pandas Timestamp) as `initial_train_size`. In this case, the initial training set will include all observations up to the specified date (inclusive)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7696e5af",
   "metadata": {},
   "outputs": [
    {
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       "      <th>fold</th>\n",
       "      <th>train_start</th>\n",
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       "      <th>last_window_start</th>\n",
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       "      <td>177</td>\n",
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       "      <td>True</td>\n",
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       "      <th>6</th>\n",
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       "      <td>0</td>\n",
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       "      <td>177</td>\n",
       "      <td>187</td>\n",
       "      <td>187</td>\n",
       "      <td>197</td>\n",
       "      <td>187</td>\n",
       "      <td>197</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
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       "      <td>197</td>\n",
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       "      <td>True</td>\n",
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      ],
      "text/plain": [
       "   fold  train_start  train_end  last_window_start  last_window_end  \\\n",
       "0     0            0        127                117              127   \n",
       "1     1            0        137                127              137   \n",
       "2     2            0        147                137              147   \n",
       "3     3            0        157                147              157   \n",
       "4     4            0        167                157              167   \n",
       "5     5            0        177                167              177   \n",
       "6     6            0        187                177              187   \n",
       "7     7            0        197                187              197   \n",
       "\n",
       "   test_start  test_end  test_start_with_gap  test_end_with_gap  \\\n",
       "0         127       137                  127                137   \n",
       "1         137       147                  137                147   \n",
       "2         147       157                  147                157   \n",
       "3         157       167                  157                167   \n",
       "4         167       177                  167                177   \n",
       "5         177       187                  177                187   \n",
       "6         187       197                  187                197   \n",
       "7         197       204                  197                204   \n",
       "\n",
       "   fit_forecaster  \n",
       "0            True  \n",
       "1            True  \n",
       "2            True  \n",
       "3            True  \n",
       "4            True  \n",
       "5            True  \n",
       "6            True  \n",
       "7            True  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create TimeSeriesFold\n",
    "# ==============================================================================\n",
    "cv = TimeSeriesFold(\n",
    "         steps                 = 10,\n",
    "         initial_train_size    = '2002-01-01 00:00:00',  # end_train\n",
    "         fold_stride           = None,\n",
    "         window_size           = 10,\n",
    "         refit                 = True,\n",
    "         fixed_train_size      = False,\n",
    "         gap                   = 0,\n",
    "         allow_incomplete_fold = True,\n",
    "         verbose               = False\n",
    "     )\n",
    "\n",
    "cv.split(X=data, as_pandas=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc24dd4d",
   "metadata": {},
   "source": [
    "When used in combination with `backtesting_forecaster`, the `window_size` argument doesn't need to be specified. This is because `backtesting_forecaster` automatically sets this value based on the configuration of the forecaster. This ensures that the backtesting process is perfectly tailored to the needs of the forecaster."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "1a929f44",
   "metadata": {},
   "source": [
    "## Backtest\n",
    "\n",
    "An example of a backtesting with refit consists of the following steps:\n",
    "\n",
    "1. Train the model using an initial training set, the length of which is specified by `initial_train_size`.\n",
    "\n",
    "2. Once the model is trained, it is used to make predictions for the next 10 steps (`steps=10`) in the data. These predictions are saved for later evaluation.\n",
    "\n",
    "3. As `refit` is set to `True`, the size of the training set is increased by adding the lats 10 data points (the previously predicted 10 steps), while the next 10 steps are used as test data.\n",
    "\n",
    "4. After expanding the training set, the model is retrained using the updated training data and then used to predict the next 10 steps.\n",
    "\n",
    "5. Repeat steps 3 and 4 until the entire series has been tested.\n",
    "\n",
    "By following these steps, you can ensure that the model is evaluated on multiple sets of test data, thereby providing a more accurate assessment of its predictive power. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "cb953a12",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-04T18:35:24.488368Z",
     "start_time": "2022-03-04T18:35:22.066351Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Information of folds\n",
      "--------------------\n",
      "Number of observations used for initial training: 127\n",
      "Number of observations used for backtesting: 77\n",
      "    Number of folds: 8\n",
      "    Number skipped folds: 0 \n",
      "    Number of steps per fold: 10\n",
      "    Number of steps to exclude between last observed data (last window) and predictions (gap): 0\n",
      "    Last fold only includes 7 observations.\n",
      "\n",
      "Fold: 0\n",
      "    Training:   1991-07-01 00:00:00 -- 2002-01-01 00:00:00  (n=127)\n",
      "    Validation: 2002-02-01 00:00:00 -- 2002-11-01 00:00:00  (n=10)\n",
      "Fold: 1\n",
      "    Training:   1991-07-01 00:00:00 -- 2002-11-01 00:00:00  (n=137)\n",
      "    Validation: 2002-12-01 00:00:00 -- 2003-09-01 00:00:00  (n=10)\n",
      "Fold: 2\n",
      "    Training:   1991-07-01 00:00:00 -- 2003-09-01 00:00:00  (n=147)\n",
      "    Validation: 2003-10-01 00:00:00 -- 2004-07-01 00:00:00  (n=10)\n",
      "Fold: 3\n",
      "    Training:   1991-07-01 00:00:00 -- 2004-07-01 00:00:00  (n=157)\n",
      "    Validation: 2004-08-01 00:00:00 -- 2005-05-01 00:00:00  (n=10)\n",
      "Fold: 4\n",
      "    Training:   1991-07-01 00:00:00 -- 2005-05-01 00:00:00  (n=167)\n",
      "    Validation: 2005-06-01 00:00:00 -- 2006-03-01 00:00:00  (n=10)\n",
      "Fold: 5\n",
      "    Training:   1991-07-01 00:00:00 -- 2006-03-01 00:00:00  (n=177)\n",
      "    Validation: 2006-04-01 00:00:00 -- 2007-01-01 00:00:00  (n=10)\n",
      "Fold: 6\n",
      "    Training:   1991-07-01 00:00:00 -- 2007-01-01 00:00:00  (n=187)\n",
      "    Validation: 2007-02-01 00:00:00 -- 2007-11-01 00:00:00  (n=10)\n",
      "Fold: 7\n",
      "    Training:   1991-07-01 00:00:00 -- 2007-11-01 00:00:00  (n=197)\n",
      "    Validation: 2007-12-01 00:00:00 -- 2008-06-01 00:00:00  (n=7)\n",
      "\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "  0%|          | 0/8 [00:00<?, ?it/s]"
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     },
     "metadata": {},
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    }
   ],
   "source": [
    "# Backtesting forecaster\n",
    "# ==============================================================================\n",
    "forecaster = ForecasterRecursive(\n",
    "                 estimator       = LGBMRegressor(random_state=123, verbose=-1),\n",
    "                 lags            = 15,\n",
    "                 window_features = RollingFeatures(stats=['mean'], window_sizes=[10])\n",
    "             )\n",
    "\n",
    "cv = TimeSeriesFold(\n",
    "        steps                 = 10,\n",
    "        initial_train_size    = len(data.loc[:end_train]),\n",
    "        refit                 = True,\n",
    "        fixed_train_size      = False,\n",
    "        gap                   = 0,\n",
    "        allow_incomplete_fold = True\n",
    "     )\n",
    "\n",
    "metric, predictions = backtesting_forecaster(\n",
    "                          forecaster    = forecaster,\n",
    "                          y             = data['y'],\n",
    "                          cv            = cv,\n",
    "                          metric        = 'mean_squared_error',\n",
    "                          n_jobs        = 'auto',\n",
    "                          verbose       = True,\n",
    "                          show_progress = True\n",
    "                      )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4c48ffbb",
   "metadata": {},
   "outputs": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fold</th>\n",
       "      <th>pred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2002-02-01</th>\n",
       "      <td>0</td>\n",
       "      <td>0.570082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-03-01</th>\n",
       "      <td>0</td>\n",
       "      <td>0.731883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-04-01</th>\n",
       "      <td>0</td>\n",
       "      <td>0.697942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-05-01</th>\n",
       "      <td>0</td>\n",
       "      <td>0.752874</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            fold      pred\n",
       "2002-02-01     0  0.570082\n",
       "2002-03-01     0  0.731883\n",
       "2002-04-01     0  0.697942\n",
       "2002-05-01     0  0.752874"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Backtest error and predictions\n",
    "# ==============================================================================\n",
    "display(metric)\n",
    "predictions.head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fb022c82",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-04T18:37:12.324865Z",
     "start_time": "2022-03-04T18:37:12.083436Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot predictions\n",
    "# ==============================================================================\n",
    "fig, ax = plt.subplots(figsize=(6, 3))\n",
    "data.loc[end_train:, 'y'].plot(ax=ax)\n",
    "predictions['pred'].plot(ax=ax)\n",
    "ax.legend()\n",
    "plt.show();"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "c1e8d685",
   "metadata": {},
   "source": [
    "## Backtesting intermittent refit\n",
    "\n",
    "The same backtesting as above is repeated, but this time with `refit = 3`. The training of the model is done every 3 folds instead of every fold."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "d6385b66",
   "metadata": {},
   "source": [
    "<div class=\"admonition note\" name=\"html-admonition\" style=\"background: rgba(0,184,212,.1); padding-top: 0px; padding-bottom: 6px; border-radius: 8px; border-left: 8px solid #00b8d4; border-color: #00b8d4; padding-left: 10px; padding-right: 10px;\">\n",
    "\n",
    "<p class=\"title\">\n",
    "    <i style=\"font-size: 18px; color:#00b8d4;\"></i>\n",
    "    <b style=\"color: #00b8d4;\">&#9998 Note</b>\n",
    "</p>\n",
    "\n",
    "With an intermittent refit, the model is trained every `refit` * `steps` observations. In this case, 3 * 10 = 30 observations. This can be easily observed in the `verbose` logs.\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b4e7c479",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Information of folds\n",
      "--------------------\n",
      "Number of observations used for initial training: 127\n",
      "Number of observations used for backtesting: 77\n",
      "    Number of folds: 8\n",
      "    Number skipped folds: 0 \n",
      "    Number of steps per fold: 10\n",
      "    Number of steps to exclude between last observed data (last window) and predictions (gap): 0\n",
      "    Last fold only includes 7 observations.\n",
      "\n",
      "Fold: 0\n",
      "    Training:   1991-07-01 00:00:00 -- 2002-01-01 00:00:00  (n=127)\n",
      "    Validation: 2002-02-01 00:00:00 -- 2002-11-01 00:00:00  (n=10)\n",
      "Fold: 1\n",
      "    Training:   No training in this fold\n",
      "    Validation: 2002-12-01 00:00:00 -- 2003-09-01 00:00:00  (n=10)\n",
      "Fold: 2\n",
      "    Training:   No training in this fold\n",
      "    Validation: 2003-10-01 00:00:00 -- 2004-07-01 00:00:00  (n=10)\n",
      "Fold: 3\n",
      "    Training:   1991-07-01 00:00:00 -- 2004-07-01 00:00:00  (n=157)\n",
      "    Validation: 2004-08-01 00:00:00 -- 2005-05-01 00:00:00  (n=10)\n",
      "Fold: 4\n",
      "    Training:   No training in this fold\n",
      "    Validation: 2005-06-01 00:00:00 -- 2006-03-01 00:00:00  (n=10)\n",
      "Fold: 5\n",
      "    Training:   No training in this fold\n",
      "    Validation: 2006-04-01 00:00:00 -- 2007-01-01 00:00:00  (n=10)\n",
      "Fold: 6\n",
      "    Training:   1991-07-01 00:00:00 -- 2007-01-01 00:00:00  (n=187)\n",
      "    Validation: 2007-02-01 00:00:00 -- 2007-11-01 00:00:00  (n=10)\n",
      "Fold: 7\n",
      "    Training:   No training in this fold\n",
      "    Validation: 2007-12-01 00:00:00 -- 2008-06-01 00:00:00  (n=7)\n",
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e6e4eb9a241f40e9aee83f02726336db",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/8 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Backtesting with intermittent refit\n",
    "# ==============================================================================\n",
    "forecaster = ForecasterRecursive(\n",
    "                 estimator       = LGBMRegressor(random_state=123, verbose=-1),\n",
    "                 lags            = 15,\n",
    "                 window_features = RollingFeatures(stats=['mean'], window_sizes=[10])\n",
    "             )\n",
    "\n",
    "cv = TimeSeriesFold(\n",
    "         steps                 = 10,\n",
    "         initial_train_size    = len(data.loc[:end_train]),\n",
    "         refit                 = 3,\n",
    "         fixed_train_size      = False,\n",
    "         gap                   = 0,\n",
    "         allow_incomplete_fold = True\n",
    "     )\n",
    "\n",
    "metric, predictions = backtesting_forecaster(\n",
    "                          forecaster    = forecaster,\n",
    "                          y             = data['y'],\n",
    "                          cv            = cv,\n",
    "                          metric        = 'mean_squared_error',\n",
    "                          verbose       = True,\n",
    "                          show_progress = True\n",
    "                      )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e0b98098",
   "metadata": {},
   "outputs": [
    {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2002-02-01</th>\n",
       "      <td>0</td>\n",
       "      <td>0.570082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-03-01</th>\n",
       "      <td>0</td>\n",
       "      <td>0.731883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-04-01</th>\n",
       "      <td>0</td>\n",
       "      <td>0.697942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-05-01</th>\n",
       "      <td>0</td>\n",
       "      <td>0.752874</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            fold      pred\n",
       "2002-02-01     0  0.570082\n",
       "2002-03-01     0  0.731883\n",
       "2002-04-01     0  0.697942\n",
       "2002-05-01     0  0.752874"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Backtest error and predictions\n",
    "# ==============================================================================\n",
    "display(metric)\n",
    "predictions.head(4)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "161622f5",
   "metadata": {},
   "source": [
    "## Backtesting with gap\n",
    "\n",
    "The gap size can be adjusted with the `gap` argument. In addition, the `allow_incomplete_fold` parameter allows the last fold to be excluded from the analysis if it doesn't have the same size as the required number of `steps`.\n",
    "\n",
    "These arguments can be used in conjunction with either refit set to `True` or `False`, depending on the needs and objectives of the use case."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "98522c3c",
   "metadata": {},
   "source": [
    "<div class=\"admonition note\" name=\"html-admonition\" style=\"background: rgba(0,184,212,.1); padding-top: 0px; padding-bottom: 6px; border-radius: 8px; border-left: 8px solid #00b8d4; border-color: #00b8d4; padding-left: 10px; padding-right: 10px;\">\n",
    "\n",
    "<p class=\"title\">\n",
    "    <i style=\"font-size: 18px; color:#00b8d4;\"></i>\n",
    "    <b style=\"color: #00b8d4;\">&#9998 Note</b>\n",
    "</p>\n",
    "\n",
    "In this example, although only the last 10 predictions (steps) are stored for model evaluation, the total number of predicted steps in each fold is 15 (steps + gap).\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4a3b351e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Information of folds\n",
      "--------------------\n",
      "Number of observations used for initial training: 127\n",
      "Number of observations used for backtesting: 77\n",
      "    Number of folds: 7\n",
      "    Number skipped folds: 0 \n",
      "    Number of steps per fold: 10\n",
      "    Number of steps to exclude between last observed data (last window) and predictions (gap): 5\n",
      "    The last 1 fold(s) have been excluded because they were incomplete.\n",
      "\n",
      "Fold: 0\n",
      "    Training:   1991-07-01 00:00:00 -- 2002-01-01 00:00:00  (n=127)\n",
      "    Validation: 2002-07-01 00:00:00 -- 2003-04-01 00:00:00  (n=10)\n",
      "Fold: 1\n",
      "    Training:   1991-07-01 00:00:00 -- 2002-11-01 00:00:00  (n=137)\n",
      "    Validation: 2003-05-01 00:00:00 -- 2004-02-01 00:00:00  (n=10)\n",
      "Fold: 2\n",
      "    Training:   1991-07-01 00:00:00 -- 2003-09-01 00:00:00  (n=147)\n",
      "    Validation: 2004-03-01 00:00:00 -- 2004-12-01 00:00:00  (n=10)\n",
      "Fold: 3\n",
      "    Training:   1991-07-01 00:00:00 -- 2004-07-01 00:00:00  (n=157)\n",
      "    Validation: 2005-01-01 00:00:00 -- 2005-10-01 00:00:00  (n=10)\n",
      "Fold: 4\n",
      "    Training:   1991-07-01 00:00:00 -- 2005-05-01 00:00:00  (n=167)\n",
      "    Validation: 2005-11-01 00:00:00 -- 2006-08-01 00:00:00  (n=10)\n",
      "Fold: 5\n",
      "    Training:   1991-07-01 00:00:00 -- 2006-03-01 00:00:00  (n=177)\n",
      "    Validation: 2006-09-01 00:00:00 -- 2007-06-01 00:00:00  (n=10)\n",
      "Fold: 6\n",
      "    Training:   1991-07-01 00:00:00 -- 2007-01-01 00:00:00  (n=187)\n",
      "    Validation: 2007-07-01 00:00:00 -- 2008-04-01 00:00:00  (n=10)\n",
      "\n"
     ]
    },
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       "  0%|          | 0/7 [00:00<?, ?it/s]"
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   ],
   "source": [
    "# Backtesting forecaster\n",
    "# ==============================================================================\n",
    "forecaster = ForecasterRecursive(\n",
    "                 estimator       = LGBMRegressor(random_state=123, verbose=-1),\n",
    "                 lags            = 15,\n",
    "                 window_features = RollingFeatures(stats=['mean'], window_sizes=[10])\n",
    "             )\n",
    "\n",
    "cv = TimeSeriesFold(\n",
    "         steps                 = 10,\n",
    "         initial_train_size    = len(data.loc[:end_train]),\n",
    "         refit                 = True,\n",
    "         fixed_train_size      = False,\n",
    "         gap                   = 5,\n",
    "         allow_incomplete_fold = False\n",
    "     )\n",
    "\n",
    "metric, predictions = backtesting_forecaster(\n",
    "                          forecaster    = forecaster,\n",
    "                          y             = data['y'],\n",
    "                          cv            = cv,\n",
    "                          metric        = 'mean_squared_error',\n",
    "                          verbose       = True,\n",
    "                          show_progress = True\n",
    "                      )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8ff6f6cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_squared_error</th>\n",
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       "   mean_squared_error\n",
       "0            0.009746"
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fold</th>\n",
       "      <th>pred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2002-07-01</th>\n",
       "      <td>0</td>\n",
       "      <td>0.847688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-08-01</th>\n",
       "      <td>0</td>\n",
       "      <td>0.907662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-01</th>\n",
       "      <td>0</td>\n",
       "      <td>0.932360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-10-01</th>\n",
       "      <td>0</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "            fold      pred\n",
       "2002-07-01     0  0.847688\n",
       "2002-08-01     0  0.907662\n",
       "2002-09-01     0  0.932360\n",
       "2002-10-01     0  0.988048"
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     "execution_count": 12,
     "metadata": {},
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   ],
   "source": [
    "# Backtest error and predictions\n",
    "# ==============================================================================\n",
    "display(metric)\n",
    "predictions.head(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21a04aea",
   "metadata": {},
   "source": [
    "## Backtesting using fold stride\n",
    "\n",
    "The parameter `fold_stride` controls how far the test window advances between consecutive folds.\n",
    "\n",
    "- If `fold_stride == steps` (default), test sets are placed back-to-back without overlap. Each observation appears only once in the output DataFrame, so the index is unique.\n",
    "\n",
    "- If `fold_stride < steps`, test sets overlap. Multiple forecasts are generated for the same observations and, therefore, the output DataFrame contains repeated indexes.\n",
    "\n",
    "- If `fold_stride > steps`, there are gaps between consecutive test sets. Some observations in the series will not have associated predictions, so the output DataFrame has non-contiguous indexes.\n",
    "\n",
    "In this example, the model predicts 10 steps ahead (`steps=10`), but the test window moves forward by only 5 steps between folds (`fold_stride=5`). As a result, each test set overlaps with the previous one by 5 steps. This means that some observations are predicted multiple times, leading to repeated indexes in the output DataFrame.\n",
    "\n",
    "The **output metric is calculated using the last prediction generated for each observation**. For example, if an observation is predicted in fold 1 and again in fold 2, only the prediction from fold 2 will be used to compute the error metric."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9dbbdddd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Information of folds\n",
      "--------------------\n",
      "Number of observations used for initial training: 127\n",
      "Number of observations used for backtesting: 77\n",
      "    Number of folds: 16\n",
      "    Number skipped folds: 0 \n",
      "    Number of steps per fold: 10\n",
      "    Number of steps to the next fold (fold stride): 5\n",
      "    Number of steps to exclude between last observed data (last window) and predictions (gap): 0\n",
      "    Last fold only includes 2 observations.\n",
      "\n",
      "Fold: 0\n",
      "    Training:   1991-07-01 00:00:00 -- 2002-01-01 00:00:00  (n=127)\n",
      "    Validation: 2002-02-01 00:00:00 -- 2002-11-01 00:00:00  (n=10)\n",
      "Fold: 1\n",
      "    Training:   1991-07-01 00:00:00 -- 2002-06-01 00:00:00  (n=132)\n",
      "    Validation: 2002-07-01 00:00:00 -- 2003-04-01 00:00:00  (n=10)\n",
      "Fold: 2\n",
      "    Training:   1991-07-01 00:00:00 -- 2002-11-01 00:00:00  (n=137)\n",
      "    Validation: 2002-12-01 00:00:00 -- 2003-09-01 00:00:00  (n=10)\n",
      "Fold: 3\n",
      "    Training:   1991-07-01 00:00:00 -- 2003-04-01 00:00:00  (n=142)\n",
      "    Validation: 2003-05-01 00:00:00 -- 2004-02-01 00:00:00  (n=10)\n",
      "Fold: 4\n",
      "    Training:   1991-07-01 00:00:00 -- 2003-09-01 00:00:00  (n=147)\n",
      "    Validation: 2003-10-01 00:00:00 -- 2004-07-01 00:00:00  (n=10)\n",
      "Fold: 5\n",
      "    Training:   1991-07-01 00:00:00 -- 2004-02-01 00:00:00  (n=152)\n",
      "    Validation: 2004-03-01 00:00:00 -- 2004-12-01 00:00:00  (n=10)\n",
      "Fold: 6\n",
      "    Training:   1991-07-01 00:00:00 -- 2004-07-01 00:00:00  (n=157)\n",
      "    Validation: 2004-08-01 00:00:00 -- 2005-05-01 00:00:00  (n=10)\n",
      "Fold: 7\n",
      "    Training:   1991-07-01 00:00:00 -- 2004-12-01 00:00:00  (n=162)\n",
      "    Validation: 2005-01-01 00:00:00 -- 2005-10-01 00:00:00  (n=10)\n",
      "Fold: 8\n",
      "    Training:   1991-07-01 00:00:00 -- 2005-05-01 00:00:00  (n=167)\n",
      "    Validation: 2005-06-01 00:00:00 -- 2006-03-01 00:00:00  (n=10)\n",
      "Fold: 9\n",
      "    Training:   1991-07-01 00:00:00 -- 2005-10-01 00:00:00  (n=172)\n",
      "    Validation: 2005-11-01 00:00:00 -- 2006-08-01 00:00:00  (n=10)\n",
      "Fold: 10\n",
      "    Training:   1991-07-01 00:00:00 -- 2006-03-01 00:00:00  (n=177)\n",
      "    Validation: 2006-04-01 00:00:00 -- 2007-01-01 00:00:00  (n=10)\n",
      "Fold: 11\n",
      "    Training:   1991-07-01 00:00:00 -- 2006-08-01 00:00:00  (n=182)\n",
      "    Validation: 2006-09-01 00:00:00 -- 2007-06-01 00:00:00  (n=10)\n",
      "Fold: 12\n",
      "    Training:   1991-07-01 00:00:00 -- 2007-01-01 00:00:00  (n=187)\n",
      "    Validation: 2007-02-01 00:00:00 -- 2007-11-01 00:00:00  (n=10)\n",
      "Fold: 13\n",
      "    Training:   1991-07-01 00:00:00 -- 2007-06-01 00:00:00  (n=192)\n",
      "    Validation: 2007-07-01 00:00:00 -- 2008-04-01 00:00:00  (n=10)\n",
      "Fold: 14\n",
      "    Training:   1991-07-01 00:00:00 -- 2007-11-01 00:00:00  (n=197)\n",
      "    Validation: 2007-12-01 00:00:00 -- 2008-06-01 00:00:00  (n=7)\n",
      "Fold: 15\n",
      "    Training:   1991-07-01 00:00:00 -- 2008-04-01 00:00:00  (n=202)\n",
      "    Validation: 2008-05-01 00:00:00 -- 2008-06-01 00:00:00  (n=2)\n",
      "\n"
     ]
    },
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       "  0%|          | 0/16 [00:00<?, ?it/s]"
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   ],
   "source": [
    "# Backtesting forecaster with fold stride\n",
    "# ==============================================================================\n",
    "forecaster = ForecasterRecursive(\n",
    "                 estimator       = LGBMRegressor(random_state=123, verbose=-1),\n",
    "                 lags            = 15,\n",
    "                 window_features = RollingFeatures(stats=['mean'], window_sizes=[10])\n",
    "             )\n",
    "\n",
    "cv = TimeSeriesFold(\n",
    "         steps                 = 10,\n",
    "         initial_train_size    = len(data.loc[:end_train]),\n",
    "         fold_stride           = 5,\n",
    "         refit                 = True,\n",
    "         fixed_train_size      = False,\n",
    "         allow_incomplete_fold = True\n",
    "     )\n",
    "\n",
    "metric, predictions = backtesting_forecaster(\n",
    "                          forecaster    = forecaster,\n",
    "                          y             = data['y'],\n",
    "                          cv            = cv,\n",
    "                          metric        = 'mean_squared_error',\n",
    "                          n_jobs        = 'auto',\n",
    "                          verbose       = True,\n",
    "                          show_progress = True\n",
    "                      )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "17028521",
   "metadata": {},
   "outputs": [
    {
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       "   mean_squared_error\n",
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>fold</th>\n",
       "      <th>pred</th>\n",
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       "  <tbody>\n",
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       "      <th>2002-02-01</th>\n",
       "      <td>0</td>\n",
       "      <td>0.570082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-03-01</th>\n",
       "      <td>0</td>\n",
       "      <td>0.731883</td>\n",
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       "    <tr>\n",
       "      <th>2002-04-01</th>\n",
       "      <td>0</td>\n",
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       "      <th>2002-05-01</th>\n",
       "      <td>0</td>\n",
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      "text/plain": [
       "            fold      pred\n",
       "2002-02-01     0  0.570082\n",
       "2002-03-01     0  0.731883\n",
       "2002-04-01     0  0.697942\n",
       "2002-05-01     0  0.752874"
      ]
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     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Backtest error and predictions\n",
    "# ==============================================================================\n",
    "display(metric)\n",
    "predictions.head(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3640770b",
   "metadata": {},
   "source": [
    "## Backtesting with skip folds\n",
    "\n",
    "+ If `skip_folds` is an integer, every 'skip_folds'-th is returned. For example, if `skip_folds = 3` and there are 10 folds, the returned folds will be [0, 3, 6, 9].\n",
    "\n",
    "+ If `skip_folds` is a list, the folds in the list are skipped. If `skip_folds = [1, 2, 3]` and there are 10 folds, the returned folds will be [0, 4, 5, 6, 7, 8, 9]."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a64f0bf7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Information of folds\n",
      "--------------------\n",
      "Number of observations used for initial training: 127\n",
      "Number of observations used for backtesting: 77\n",
      "    Number of folds: 8\n",
      "    Number skipped folds: 4 [1, 3, 5, 7]\n",
      "    Number of steps per fold: 10\n",
      "    Number of steps to exclude between last observed data (last window) and predictions (gap): 0\n",
      "    Last fold only includes 7 observations.\n",
      "\n",
      "Fold: 0\n",
      "    Training:   1991-07-01 00:00:00 -- 2002-01-01 00:00:00  (n=127)\n",
      "    Validation: 2002-02-01 00:00:00 -- 2002-11-01 00:00:00  (n=10)\n",
      "Fold: 1\n",
      "    Fold skipped\n",
      "Fold: 2\n",
      "    Training:   1991-07-01 00:00:00 -- 2003-09-01 00:00:00  (n=147)\n",
      "    Validation: 2003-10-01 00:00:00 -- 2004-07-01 00:00:00  (n=10)\n",
      "Fold: 3\n",
      "    Fold skipped\n",
      "Fold: 4\n",
      "    Training:   1991-07-01 00:00:00 -- 2005-05-01 00:00:00  (n=167)\n",
      "    Validation: 2005-06-01 00:00:00 -- 2006-03-01 00:00:00  (n=10)\n",
      "Fold: 5\n",
      "    Fold skipped\n",
      "Fold: 6\n",
      "    Training:   1991-07-01 00:00:00 -- 2007-01-01 00:00:00  (n=187)\n",
      "    Validation: 2007-02-01 00:00:00 -- 2007-11-01 00:00:00  (n=10)\n",
      "Fold: 7\n",
      "    Fold skipped\n",
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "583ec1086a0e4a4cb5f888a8a1d8e988",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/4 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Backtesting forecaster with refit and skip folds\n",
    "# ==============================================================================\n",
    "forecaster = ForecasterRecursive(\n",
    "                 estimator       = LGBMRegressor(random_state=123, verbose=-1),\n",
    "                 lags            = 15,\n",
    "                 window_features = RollingFeatures(stats=['mean'], window_sizes=[10])\n",
    "             )\n",
    "\n",
    "cv = TimeSeriesFold(\n",
    "         steps                 = 10,\n",
    "         initial_train_size    = len(data.loc[:end_train]),\n",
    "         refit                 = True,\n",
    "         fixed_train_size      = False,\n",
    "         gap                   = 0,\n",
    "         skip_folds            = 2,\n",
    "         allow_incomplete_fold = True\n",
    "     )\n",
    "\n",
    "metric, predictions = backtesting_forecaster(\n",
    "                          forecaster    = forecaster,\n",
    "                          y             = data['y'],\n",
    "                          cv            = cv,\n",
    "                          metric        = 'mean_squared_error',\n",
    "                          n_jobs        = 'auto',\n",
    "                          verbose       = True,\n",
    "                          show_progress = True\n",
    "                      )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "04377eb7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_squared_error</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00537</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean_squared_error\n",
       "0             0.00537"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fold</th>\n",
       "      <th>pred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2002-02-01</th>\n",
       "      <td>0</td>\n",
       "      <td>0.570082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-03-01</th>\n",
       "      <td>0</td>\n",
       "      <td>0.731883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-04-01</th>\n",
       "      <td>0</td>\n",
       "      <td>0.697942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-05-01</th>\n",
       "      <td>0</td>\n",
       "      <td>0.752874</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            fold      pred\n",
       "2002-02-01     0  0.570082\n",
       "2002-03-01     0  0.731883\n",
       "2002-04-01     0  0.697942\n",
       "2002-05-01     0  0.752874"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Backtest error and predictions\n",
    "# ==============================================================================\n",
    "display(metric)\n",
    "predictions.head(4)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "a64e85b6",
   "metadata": {},
   "source": [
    "## Backtesting with prediction intervals\n",
    "\n",
    "Backtesting can be used not only to obtain point estimate predictions but also to obtain prediction intervals. Prediction intervals provide a range of values within which the actual values are expected to fall with a certain level of confidence. By estimating prediction intervals during backtesting, one can get a better understanding of the uncertainty associated with your model's predictions. This information can be used to evaluate the reliability of the model's predictions and to make more informed decisions.\n",
    "\n",
    "To learn more about probabilistic forecasting features available in skforecast, see [Probabilistic forecasting](../user_guides/probabilistic-forecasting-overview.html). \n",
    "\n",
    "The `interval` argument can be specified as:\n",
    "\n",
    "- If `list` or `tuple`: Sequence of percentiles to compute, each value must be between 0 and 100 inclusive. For example, a 95% confidence interval can be specified as `interval = [2.5, 97.5]` or multiple percentiles (e.g. 10, 50 and 90) as `interval = [10, 50, 90]`.\n",
    "\n",
    "- If `'bootstrapping'` (str): `n_boot` bootstrapping predictions will be generated.\n",
    "\n",
    "- If `scipy.stats distribution object`, the distribution parameters will be estimated for each prediction.\n",
    "\n",
    "- If `None`, no probabilistic predictions are estimated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "773e4845",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-04T18:37:20.175054Z",
     "start_time": "2022-03-04T18:37:14.748117Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b1b48169adea43bcaa027c8f8c69dbeb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/8 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Backtesting forecaster with prediction intervals\n",
    "# ==============================================================================\n",
    "forecaster = ForecasterRecursive(\n",
    "                 estimator       = Ridge(random_state=123),\n",
    "                 lags            = 15,\n",
    "                 window_features = RollingFeatures(stats=['mean'], window_sizes=[10])\n",
    "             )\n",
    "\n",
    "cv = TimeSeriesFold(\n",
    "         steps                 = 10,\n",
    "         initial_train_size    = len(data.loc[:end_train]),\n",
    "         refit                 = True,\n",
    "         fixed_train_size      = False,\n",
    "         gap                   = 0,\n",
    "         skip_folds            = None,\n",
    "         allow_incomplete_fold = True\n",
    "     )\n",
    "\n",
    "metric, predictions = backtesting_forecaster(\n",
    "                          forecaster              = forecaster,\n",
    "                          y                       = data['y'],\n",
    "                          cv                      = cv,\n",
    "                          metric                  = 'mean_squared_error',\n",
    "                          interval_method         = 'bootstrapping',\n",
    "                          interval                = [5, 95],\n",
    "                          n_boot                  = 150,\n",
    "                          use_in_sample_residuals = True,\n",
    "                          verbose                 = False,\n",
    "                          show_progress           = True\n",
    "                      )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "9b032173",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fold</th>\n",
       "      <th>pred</th>\n",
       "      <th>lower_bound</th>\n",
       "      <th>upper_bound</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2002-02-01</th>\n",
       "      <td>0</td>\n",
       "      <td>0.704045</td>\n",
       "      <td>0.517564</td>\n",
       "      <td>0.798605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-03-01</th>\n",
       "      <td>0</td>\n",
       "      <td>0.674050</td>\n",
       "      <td>0.574914</td>\n",
       "      <td>0.781793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-04-01</th>\n",
       "      <td>0</td>\n",
       "      <td>0.698856</td>\n",
       "      <td>0.522447</td>\n",
       "      <td>0.792217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-05-01</th>\n",
       "      <td>0</td>\n",
       "      <td>0.703732</td>\n",
       "      <td>0.532736</td>\n",
       "      <td>0.806036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-06-01</th>\n",
       "      <td>0</td>\n",
       "      <td>0.734222</td>\n",
       "      <td>0.553879</td>\n",
       "      <td>0.825653</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            fold      pred  lower_bound  upper_bound\n",
       "2002-02-01     0  0.704045     0.517564     0.798605\n",
       "2002-03-01     0  0.674050     0.574914     0.781793\n",
       "2002-04-01     0  0.698856     0.522447     0.792217\n",
       "2002-05-01     0  0.703732     0.532736     0.806036\n",
       "2002-06-01     0  0.734222     0.553879     0.825653"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Interval predictions\n",
    "# ==============================================================================\n",
    "predictions.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e127fd39",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-04T18:37:33.887394Z",
     "start_time": "2022-03-04T18:37:33.623141Z"
    }
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 700x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot prediction interval\n",
    "# ==============================================================================\n",
    "fig, ax = plt.subplots(figsize=(7, 3))\n",
    "plot_prediction_intervals(\n",
    "    predictions         = predictions,\n",
    "    y_true              = data['y'],\n",
    "    target_variable     = 'y',\n",
    "    title               = \"Prediction intervals\",\n",
    "    kwargs_fill_between = {'color': 'gray', 'alpha': 0.3, 'zorder': 1},\n",
    "    ax                  = ax\n",
    ")\n",
    "ax.legend(loc='upper left');"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "f5b827f0",
   "metadata": {},
   "source": [
    "## Backtesting with exogenous variables\n",
    "\n",
    "All the backtesting strategies discussed in this document can also be applied when incorporating [exogenous variables](../user_guides/exogenous-variables.html) in the forecasting model.\n",
    "\n",
    "Exogenous variables are additional independent variables that can impact the value of the target variable being forecasted. These variables can provide valuable information to the model and improve its forecasting accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "80118409",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-04T18:39:28.763030Z",
     "start_time": "2022-03-04T18:39:28.259387Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">╭─────────────────────────────────── <span style=\"font-weight: bold\">h2o_exog</span> ────────────────────────────────────╮\n",
       "│ <span style=\"font-weight: bold\">Description:</span>                                                                    │\n",
       "│ Monthly expenditure ($AUD) on corticosteroid drugs that the Australian health   │\n",
       "│ system had between 1991 and 2008. Two additional variables (exog_1, exog_2) are │\n",
       "│ simulated.                                                                      │\n",
       "│                                                                                 │\n",
       "│ <span style=\"font-weight: bold\">Source:</span>                                                                         │\n",
       "│ Hyndman R (2023). fpp3: Data for Forecasting: Principles and Practice (3rd      │\n",
       "│ Edition). http://pkg.robjhyndman.com/fpp3package/,                              │\n",
       "│ https://github.com/robjhyndman/fpp3package, http://OTexts.com/fpp3.             │\n",
       "│                                                                                 │\n",
       "│ <span style=\"font-weight: bold\">URL:</span>                                                                            │\n",
       "│ https://raw.githubusercontent.com/skforecast/skforecast-                        │\n",
       "│ datasets/main/data/h2o_exog.csv                                                 │\n",
       "│                                                                                 │\n",
       "│ <span style=\"font-weight: bold\">Shape:</span> 195 rows x 3 columns                                                     │\n",
       "╰─────────────────────────────────────────────────────────────────────────────────╯\n",
       "</pre>\n"
      ],
      "text/plain": [
       "╭─────────────────────────────────── \u001b[1mh2o_exog\u001b[0m ────────────────────────────────────╮\n",
       "│ \u001b[1mDescription:\u001b[0m                                                                    │\n",
       "│ Monthly expenditure ($AUD) on corticosteroid drugs that the Australian health   │\n",
       "│ system had between 1991 and 2008. Two additional variables (exog_1, exog_2) are │\n",
       "│ simulated.                                                                      │\n",
       "│                                                                                 │\n",
       "│ \u001b[1mSource:\u001b[0m                                                                         │\n",
       "│ Hyndman R (2023). fpp3: Data for Forecasting: Principles and Practice (3rd      │\n",
       "│ Edition). http://pkg.robjhyndman.com/fpp3package/,                              │\n",
       "│ https://github.com/robjhyndman/fpp3package, http://OTexts.com/fpp3.             │\n",
       "│                                                                                 │\n",
       "│ \u001b[1mURL:\u001b[0m                                                                            │\n",
       "│ https://raw.githubusercontent.com/skforecast/skforecast-                        │\n",
       "│ datasets/main/data/h2o_exog.csv                                                 │\n",
       "│                                                                                 │\n",
       "│ \u001b[1mShape:\u001b[0m 195 rows x 3 columns                                                     │\n",
       "╰─────────────────────────────────────────────────────────────────────────────────╯\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Download data\n",
    "# ==============================================================================\n",
    "data = fetch_dataset(name='h2o_exog', raw=False)\n",
    "data.index.name = 'datetime'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "2e4b4f48",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train dates      : 1992-04-01 00:00:00 --- 2002-01-01 00:00:00  (n=118)\n",
      "Validation dates : 2002-02-01 00:00:00 --- 2008-06-01 00:00:00  (n=77)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Train-validation dates\n",
    "# ==============================================================================\n",
    "end_train = '2002-01-01 23:59:00'\n",
    "\n",
    "print(\n",
    "    f\"Train dates      : {data.index.min()} --- {data.loc[:end_train].index.max()}\"\n",
    "    f\"  (n={len(data.loc[:end_train])})\"\n",
    ")\n",
    "print(\n",
    "    f\"Validation dates : {data.loc[end_train:].index.min()} --- {data.index.max()}\"\n",
    "    f\"  (n={len(data.loc[end_train:])})\"\n",
    ")\n",
    "\n",
    "# Plot\n",
    "# ==============================================================================\n",
    "fig, ax = plt.subplots(figsize=(6, 3))\n",
    "data.plot(ax=ax);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "11d9d4d9",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-04T18:39:31.902840Z",
     "start_time": "2022-03-04T18:39:29.415044Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c2ab7be61157435da2ac6cef054dec0d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/8 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Backtest forecaster exogenous variables\n",
    "# ==============================================================================\n",
    "forecaster = ForecasterRecursive(\n",
    "                 estimator       = LGBMRegressor(random_state=123, verbose=-1),\n",
    "                 lags            = 15,\n",
    "                 window_features = RollingFeatures(stats=['mean'], window_sizes=[10])\n",
    "             )\n",
    "\n",
    "cv = TimeSeriesFold(\n",
    "         steps                 = 10,\n",
    "         initial_train_size    = len(data.loc[:end_train]),\n",
    "         refit                 = True,\n",
    "         fixed_train_size      = False,\n",
    "         gap                   = 0,\n",
    "         skip_folds            = None,\n",
    "         allow_incomplete_fold = True\n",
    "     )\n",
    "\n",
    "metric, predictions = backtesting_forecaster(\n",
    "                          forecaster = forecaster,\n",
    "                          y          = data['y'],\n",
    "                          exog       = data[['exog_1', 'exog_2']],\n",
    "                          cv         = cv,\n",
    "                          metric     = 'mean_squared_error'\n",
    "                      )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "583e78d3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-04T18:39:32.005082Z",
     "start_time": "2022-03-04T18:39:31.988073Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Backtest error with exogenous variables:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_squared_error</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.007147</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean_squared_error\n",
       "0            0.007147"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Backtest error\n",
    "# ==============================================================================\n",
    "print(\"Backtest error with exogenous variables:\")\n",
    "metric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "dca040ef",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-03-04T18:39:33.339158Z",
     "start_time": "2022-03-04T18:39:33.029390Z"
    },
    "jupyter": {
     "source_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot predictions\n",
    "# ==============================================================================\n",
    "fig, ax = plt.subplots(figsize=(6, 3))\n",
    "data.loc[end_train:].plot(ax=ax)\n",
    "predictions['pred'].plot(ax=ax)\n",
    "ax.legend(loc=\"upper left\")\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c161c3b",
   "metadata": {},
   "source": [
    "## Return predictor's values\n",
    "\n",
    "In addition to the forecasted values, the backtesting process can also return the predictor values used to generate each prediction. This can be enabled by setting the argument `return_predictors=True`. Also, a `fold` column is added to the returned DataFrame, indicating the fold to which each observation belongs. This allows for a more detailed analysis of the model's performance and the factors that influenced its predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "3cd4cbb1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fold</th>\n",
       "      <th>train_start</th>\n",
       "      <th>train_end</th>\n",
       "      <th>last_window_start</th>\n",
       "      <th>last_window_end</th>\n",
       "      <th>test_start</th>\n",
       "      <th>test_end</th>\n",
       "      <th>test_start_with_gap</th>\n",
       "      <th>test_end_with_gap</th>\n",
       "      <th>fit_forecaster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>118</td>\n",
       "      <td>108</td>\n",
       "      <td>118</td>\n",
       "      <td>118</td>\n",
       "      <td>128</td>\n",
       "      <td>118</td>\n",
       "      <td>128</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>128</td>\n",
       "      <td>118</td>\n",
       "      <td>128</td>\n",
       "      <td>128</td>\n",
       "      <td>138</td>\n",
       "      <td>128</td>\n",
       "      <td>138</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>138</td>\n",
       "      <td>128</td>\n",
       "      <td>138</td>\n",
       "      <td>138</td>\n",
       "      <td>148</td>\n",
       "      <td>138</td>\n",
       "      <td>148</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>148</td>\n",
       "      <td>138</td>\n",
       "      <td>148</td>\n",
       "      <td>148</td>\n",
       "      <td>158</td>\n",
       "      <td>148</td>\n",
       "      <td>158</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>158</td>\n",
       "      <td>148</td>\n",
       "      <td>158</td>\n",
       "      <td>158</td>\n",
       "      <td>168</td>\n",
       "      <td>158</td>\n",
       "      <td>168</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>168</td>\n",
       "      <td>158</td>\n",
       "      <td>168</td>\n",
       "      <td>168</td>\n",
       "      <td>178</td>\n",
       "      <td>168</td>\n",
       "      <td>178</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>178</td>\n",
       "      <td>168</td>\n",
       "      <td>178</td>\n",
       "      <td>178</td>\n",
       "      <td>188</td>\n",
       "      <td>178</td>\n",
       "      <td>188</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>188</td>\n",
       "      <td>178</td>\n",
       "      <td>188</td>\n",
       "      <td>188</td>\n",
       "      <td>195</td>\n",
       "      <td>188</td>\n",
       "      <td>195</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fold  train_start  train_end  last_window_start  last_window_end  \\\n",
       "0     0            0        118                108              118   \n",
       "1     1            0        128                118              128   \n",
       "2     2            0        138                128              138   \n",
       "3     3            0        148                138              148   \n",
       "4     4            0        158                148              158   \n",
       "5     5            0        168                158              168   \n",
       "6     6            0        178                168              178   \n",
       "7     7            0        188                178              188   \n",
       "\n",
       "   test_start  test_end  test_start_with_gap  test_end_with_gap  \\\n",
       "0         118       128                  118                128   \n",
       "1         128       138                  128                138   \n",
       "2         138       148                  138                148   \n",
       "3         148       158                  148                158   \n",
       "4         158       168                  158                168   \n",
       "5         168       178                  168                178   \n",
       "6         178       188                  178                188   \n",
       "7         188       195                  188                195   \n",
       "\n",
       "   fit_forecaster  \n",
       "0            True  \n",
       "1            True  \n",
       "2            True  \n",
       "3            True  \n",
       "4            True  \n",
       "5            True  \n",
       "6            True  \n",
       "7            True  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create TimeSeriesFold\n",
    "# ==============================================================================\n",
    "cv = TimeSeriesFold(\n",
    "         steps                 = 10,\n",
    "         initial_train_size    = '2002-01-01 00:00:00',\n",
    "         window_size           = 10,\n",
    "         refit                 = True,\n",
    "         fixed_train_size      = False,\n",
    "         gap                   = 0,\n",
    "         allow_incomplete_fold = True,\n",
    "         verbose               = False\n",
    "     )\n",
    "\n",
    "cv.split(X=data, as_pandas=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "f721025c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e2ddbbcdcd654735a79a6b5cb6b1f6f3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/8 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fold</th>\n",
       "      <th>pred</th>\n",
       "      <th>lag_1</th>\n",
       "      <th>lag_2</th>\n",
       "      <th>lag_3</th>\n",
       "      <th>lag_4</th>\n",
       "      <th>lag_5</th>\n",
       "      <th>lag_6</th>\n",
       "      <th>lag_7</th>\n",
       "      <th>lag_8</th>\n",
       "      <th>lag_9</th>\n",
       "      <th>lag_10</th>\n",
       "      <th>lag_11</th>\n",
       "      <th>lag_12</th>\n",
       "      <th>lag_13</th>\n",
       "      <th>lag_14</th>\n",
       "      <th>lag_15</th>\n",
       "      <th>roll_mean_10</th>\n",
       "      <th>exog_1</th>\n",
       "      <th>exog_2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2002-02-01</th>\n",
       "      <td>0</td>\n",
       "      <td>0.618160</td>\n",
       "      <td>1.145868</td>\n",
       "      <td>1.012313</td>\n",
       "      <td>1.109590</td>\n",
       "      <td>1.024293</td>\n",
       "      <td>0.867444</td>\n",
       "      <td>0.907705</td>\n",
       "      <td>0.851926</td>\n",
       "      <td>0.699431</td>\n",
       "      <td>0.704115</td>\n",
       "      <td>0.648470</td>\n",
       "      <td>0.672569</td>\n",
       "      <td>0.510647</td>\n",
       "      <td>1.043805</td>\n",
       "      <td>1.013024</td>\n",
       "      <td>0.938596</td>\n",
       "      <td>0.897115</td>\n",
       "      <td>1.389827</td>\n",
       "      <td>1.673530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-03-01</th>\n",
       "      <td>0</td>\n",
       "      <td>0.742523</td>\n",
       "      <td>0.618160</td>\n",
       "      <td>1.145868</td>\n",
       "      <td>1.012313</td>\n",
       "      <td>1.109590</td>\n",
       "      <td>1.024293</td>\n",
       "      <td>0.867444</td>\n",
       "      <td>0.907705</td>\n",
       "      <td>0.851926</td>\n",
       "      <td>0.699431</td>\n",
       "      <td>0.704115</td>\n",
       "      <td>0.648470</td>\n",
       "      <td>0.672569</td>\n",
       "      <td>0.510647</td>\n",
       "      <td>1.043805</td>\n",
       "      <td>1.013024</td>\n",
       "      <td>0.894085</td>\n",
       "      <td>1.383532</td>\n",
       "      <td>1.596904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-04-01</th>\n",
       "      <td>0</td>\n",
       "      <td>0.685457</td>\n",
       "      <td>0.742523</td>\n",
       "      <td>0.618160</td>\n",
       "      <td>1.145868</td>\n",
       "      <td>1.012313</td>\n",
       "      <td>1.109590</td>\n",
       "      <td>1.024293</td>\n",
       "      <td>0.867444</td>\n",
       "      <td>0.907705</td>\n",
       "      <td>0.851926</td>\n",
       "      <td>0.699431</td>\n",
       "      <td>0.704115</td>\n",
       "      <td>0.648470</td>\n",
       "      <td>0.672569</td>\n",
       "      <td>0.510647</td>\n",
       "      <td>1.043805</td>\n",
       "      <td>0.897925</td>\n",
       "      <td>1.381575</td>\n",
       "      <td>1.510958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-05-01</th>\n",
       "      <td>0</td>\n",
       "      <td>0.775086</td>\n",
       "      <td>0.685457</td>\n",
       "      <td>0.742523</td>\n",
       "      <td>0.618160</td>\n",
       "      <td>1.145868</td>\n",
       "      <td>1.012313</td>\n",
       "      <td>1.109590</td>\n",
       "      <td>1.024293</td>\n",
       "      <td>0.867444</td>\n",
       "      <td>0.907705</td>\n",
       "      <td>0.851926</td>\n",
       "      <td>0.699431</td>\n",
       "      <td>0.704115</td>\n",
       "      <td>0.648470</td>\n",
       "      <td>0.672569</td>\n",
       "      <td>0.510647</td>\n",
       "      <td>0.896528</td>\n",
       "      <td>1.373176</td>\n",
       "      <td>1.462083</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            fold      pred     lag_1     lag_2     lag_3     lag_4     lag_5  \\\n",
       "2002-02-01     0  0.618160  1.145868  1.012313  1.109590  1.024293  0.867444   \n",
       "2002-03-01     0  0.742523  0.618160  1.145868  1.012313  1.109590  1.024293   \n",
       "2002-04-01     0  0.685457  0.742523  0.618160  1.145868  1.012313  1.109590   \n",
       "2002-05-01     0  0.775086  0.685457  0.742523  0.618160  1.145868  1.012313   \n",
       "\n",
       "               lag_6     lag_7     lag_8     lag_9    lag_10    lag_11  \\\n",
       "2002-02-01  0.907705  0.851926  0.699431  0.704115  0.648470  0.672569   \n",
       "2002-03-01  0.867444  0.907705  0.851926  0.699431  0.704115  0.648470   \n",
       "2002-04-01  1.024293  0.867444  0.907705  0.851926  0.699431  0.704115   \n",
       "2002-05-01  1.109590  1.024293  0.867444  0.907705  0.851926  0.699431   \n",
       "\n",
       "              lag_12    lag_13    lag_14    lag_15  roll_mean_10    exog_1  \\\n",
       "2002-02-01  0.510647  1.043805  1.013024  0.938596      0.897115  1.389827   \n",
       "2002-03-01  0.672569  0.510647  1.043805  1.013024      0.894085  1.383532   \n",
       "2002-04-01  0.648470  0.672569  0.510647  1.043805      0.897925  1.381575   \n",
       "2002-05-01  0.704115  0.648470  0.672569  0.510647      0.896528  1.373176   \n",
       "\n",
       "              exog_2  \n",
       "2002-02-01  1.673530  \n",
       "2002-03-01  1.596904  \n",
       "2002-04-01  1.510958  \n",
       "2002-05-01  1.462083  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Return predictors values used in backtesting\n",
    "# ==============================================================================\n",
    "metric, predictions = backtesting_forecaster(\n",
    "                          forecaster        = forecaster,\n",
    "                          y                 = data['y'],\n",
    "                          exog              = data[['exog_1', 'exog_2']],\n",
    "                          cv                = cv,\n",
    "                          metric            = 'mean_squared_error',\n",
    "                          return_predictors = True\n",
    "                      )\n",
    "predictions.head(4)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "fe80d168",
   "metadata": {},
   "source": [
    "## Backtesting with custom metric\n",
    "\n",
    "In addition to the commonly used metrics such as mean_squared_error, mean_absolute_error, and mean_absolute_percentage_error, users have the flexibility to define their own custom metric function, provided that it includes the arguments `y_true` (the true values of the series) and `y_pred` (the predicted values), and returns a numeric value (either a `float` or an `int`).\n",
    "\n",
    "This customizability enables users to evaluate the model's predictive performance in a wide range of scenarios, such as considering only certain months, days, non holiday; or focusing only on the last step of the predicted horizon.\n",
    "\n",
    "To illustrate this, consider the following example: a 12-month horizon is forecasted, but the interest metric is calculated by considering only the last three months of each year. This is achieved by defining a custom metric function that takes into account only the relevant months, which is then passed as an argument to the backtesting function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "57ebb4c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>custom_metric</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.007179</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   custom_metric\n",
       "0       0.007179"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Backtesting with custom metric\n",
    "# ==============================================================================\n",
    "def custom_metric(y_true, y_pred):\n",
    "    \"\"\"\n",
    "    Calculate the mean squared error using only the predicted values of the last\n",
    "    3 months of the year.\n",
    "    \"\"\"\n",
    "    mask = y_true.index.month.isin([10, 11, 12])\n",
    "    metric = mean_squared_error(y_true[mask], y_pred[mask])\n",
    "    \n",
    "    return metric\n",
    "\n",
    "\n",
    "cv = TimeSeriesFold(\n",
    "         steps                 = 10,\n",
    "         initial_train_size    = len(data.loc[:end_train]),\n",
    "         refit                 = True,\n",
    "         fixed_train_size      = False,\n",
    "         gap                   = 0,\n",
    "         skip_folds            = None,\n",
    "         allow_incomplete_fold = True\n",
    "     )\n",
    "\n",
    "metric, predictions = backtesting_forecaster(\n",
    "                          forecaster = forecaster,\n",
    "                          y          = data['y'],\n",
    "                          cv         = cv,\n",
    "                          metric     = custom_metric\n",
    "                      )\n",
    "\n",
    "metric"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "d4389573",
   "metadata": {},
   "source": [
    "## Backtesting with multiple metrics\n",
    "\n",
    "The `backtesting_forecaster` function provides a convenient way to estimate multiple metrics simultaneously by accepting a list of metrics as an input argument. This list can include any combination of built-in metrics, such as mean_squared_error, mean_absolute_error, and mean_absolute_percentage_error, as well as user-defined custom metrics.\n",
    "\n",
    "By specifying multiple metrics, users can obtain a more comprehensive evaluation of the model's predictive performance, which can help in selecting the best model for a particular task. Additionally, the ability to include custom metrics allows users to tailor the evaluation to specific use cases and domain-specific requirements."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "8587c187",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "051744da99c94cc085d49f30d4301e15",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_squared_error</th>\n",
       "      <th>mean_absolute_error</th>\n",
       "      <th>custom_metric</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.007042</td>\n",
       "      <td>0.067956</td>\n",
       "      <td>0.007179</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean_squared_error  mean_absolute_error  custom_metric\n",
       "0            0.007042             0.067956       0.007179"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Backtesting with multiple metric\n",
    "# ==============================================================================\n",
    "multiple_metrics = ['mean_squared_error', 'mean_absolute_error', custom_metric]\n",
    "\n",
    "cv = TimeSeriesFold(\n",
    "         steps                 = 10,\n",
    "         initial_train_size    = len(data.loc[:end_train]),\n",
    "         refit                 = True,\n",
    "         fixed_train_size      = False,\n",
    "         gap                   = 0,\n",
    "         skip_folds            = None,\n",
    "         allow_incomplete_fold = True\n",
    "     )\n",
    "\n",
    "metrics, predictions = backtesting_forecaster(\n",
    "                           forecaster = forecaster,\n",
    "                           y          = data['y'],\n",
    "                           cv         = cv,\n",
    "                           metric     = multiple_metrics\n",
    "                       )\n",
    "\n",
    "metrics"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "e6ca1c34",
   "metadata": {},
   "source": [
    "## Backtesting on training data\n",
    "\n",
    "While the primary goal of building forecasting models is to predict future values, it is equally important to evaluate if the model is effectively learning from the training data. Analyzing predictions on the training data or exploring the prediction matrices is crucial for assessing model performance and understanding areas for optimization. This process can help identify issues like overfitting or underfitting, as well as provide deeper insights into the model’s decision-making process.\n",
    "\n",
    "To obtain predictions on the training data,the forecaster must first be fitted using the training dataset. Then, backtesting can be performed using  `backtesting_forecaster` and specifying the arguments `initial_train_size=None` and `refit=False`. This configuration enables backtesting on the same data that was used to train the model."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3093c7b6",
   "metadata": {},
   "source": [
    "<div class=\"admonition note\" name=\"html-admonition\" style=\"background: rgba(0,191,191,.1); padding-top: 0px; padding-bottom: 6px; border-radius: 8px; border-left: 8px solid #00bfa5; border-color: #00bfa5; padding-left: 10px; padding-right: 10px;\">\n",
    "\n",
    "<p class=\"title\">\n",
    "    <i style=\"font-size: 18px; color:#00bfa5;\"></i>\n",
    "    <b style=\"color: #00bfa5;\">&#128161 Tip</b>\n",
    "</p>\n",
    "\n",
    "To learn more about how to extract the training and prediction matrices, visit the following link: <a href=\"../user_guides/training-and-prediction-matrices.html\">How to Extract Training and Prediction Matrices</a>.\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "5df3b1d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create and fit forecaster\n",
    "# ==============================================================================\n",
    "forecaster = ForecasterRecursive(\n",
    "                 estimator       = LGBMRegressor(random_state=123, verbose=-1),\n",
    "                 lags            = 15,\n",
    "                 window_features = RollingFeatures(stats=['mean'], window_sizes=[10])\n",
    "             )\n",
    "\n",
    "forecaster.fit(y=data['y'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "ae09965e",
   "metadata": {},
   "outputs": [
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      ]
     },
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    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_squared_error</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000709</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean_squared_error\n",
       "0            0.000709"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Backtesting on training data\n",
    "# ==============================================================================\n",
    "cv = TimeSeriesFold(\n",
    "         steps              = 1,\n",
    "         initial_train_size = None,\n",
    "         refit              = False\n",
    "     )\n",
    "\n",
    "metric, predictions_training = backtesting_forecaster(\n",
    "                                   forecaster = forecaster,\n",
    "                                   y          = data['y'],\n",
    "                                   cv         = cv,\n",
    "                                   metric     = 'mean_squared_error'\n",
    "                               )\n",
    "\n",
    "metric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "8eba1580",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fold</th>\n",
       "      <th>pred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1993-07-01</th>\n",
       "      <td>0</td>\n",
       "      <td>0.525134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993-08-01</th>\n",
       "      <td>1</td>\n",
       "      <td>0.569934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993-09-01</th>\n",
       "      <td>2</td>\n",
       "      <td>0.601695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993-10-01</th>\n",
       "      <td>3</td>\n",
       "      <td>0.641750</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            fold      pred\n",
       "1993-07-01     0  0.525134\n",
       "1993-08-01     1  0.569934\n",
       "1993-09-01     2  0.601695\n",
       "1993-10-01     3  0.641750"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Training predictions\n",
    "# ==============================================================================\n",
    "predictions_training.head(4)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "57fba30f",
   "metadata": {},
   "source": [
    "It is important to note that the first 15 observations are excluded from the predictions since they are required to generate the lags that serve as predictors in the model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "03ac3927",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot training predictions\n",
    "# ==============================================================================\n",
    "fig, ax = plt.subplots(figsize=(6, 3))\n",
    "data['y'].plot(ax=ax)\n",
    "predictions_training['pred'].plot(ax=ax)\n",
    "ax.set_title(\"Backtesting on training data\")\n",
    "ax.legend()\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34977301",
   "metadata": {},
   "source": [
    "## Create your own backtesting GIF\n",
    "\n",
    "Visualizing backtesting strategy as an animation is a powerful way to understand how train and test windows move across a time series. By creating a GIF, you can clearly see how folds are generated, how predictions are made, and how parameters such as `steps`, `fold_stride`, or `refit` affect the process. This makes it easier to explain forecasting strategies, debug your setup, or share results in presentations and documentation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "08f166ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fold</th>\n",
       "      <th>train_start</th>\n",
       "      <th>train_end</th>\n",
       "      <th>last_window_start</th>\n",
       "      <th>last_window_end</th>\n",
       "      <th>test_start</th>\n",
       "      <th>test_end</th>\n",
       "      <th>test_start_with_gap</th>\n",
       "      <th>test_end_with_gap</th>\n",
       "      <th>fit_forecaster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>96</td>\n",
       "      <td>84</td>\n",
       "      <td>96</td>\n",
       "      <td>96</td>\n",
       "      <td>106</td>\n",
       "      <td>96</td>\n",
       "      <td>106</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>106</td>\n",
       "      <td>94</td>\n",
       "      <td>106</td>\n",
       "      <td>106</td>\n",
       "      <td>116</td>\n",
       "      <td>106</td>\n",
       "      <td>116</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>116</td>\n",
       "      <td>104</td>\n",
       "      <td>116</td>\n",
       "      <td>116</td>\n",
       "      <td>126</td>\n",
       "      <td>116</td>\n",
       "      <td>126</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>30</td>\n",
       "      <td>126</td>\n",
       "      <td>114</td>\n",
       "      <td>126</td>\n",
       "      <td>126</td>\n",
       "      <td>136</td>\n",
       "      <td>126</td>\n",
       "      <td>136</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>40</td>\n",
       "      <td>136</td>\n",
       "      <td>124</td>\n",
       "      <td>136</td>\n",
       "      <td>136</td>\n",
       "      <td>146</td>\n",
       "      <td>136</td>\n",
       "      <td>146</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "      <td>146</td>\n",
       "      <td>134</td>\n",
       "      <td>146</td>\n",
       "      <td>146</td>\n",
       "      <td>156</td>\n",
       "      <td>146</td>\n",
       "      <td>156</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>60</td>\n",
       "      <td>156</td>\n",
       "      <td>144</td>\n",
       "      <td>156</td>\n",
       "      <td>156</td>\n",
       "      <td>166</td>\n",
       "      <td>156</td>\n",
       "      <td>166</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>70</td>\n",
       "      <td>166</td>\n",
       "      <td>154</td>\n",
       "      <td>166</td>\n",
       "      <td>166</td>\n",
       "      <td>176</td>\n",
       "      <td>166</td>\n",
       "      <td>176</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>80</td>\n",
       "      <td>176</td>\n",
       "      <td>164</td>\n",
       "      <td>176</td>\n",
       "      <td>176</td>\n",
       "      <td>186</td>\n",
       "      <td>176</td>\n",
       "      <td>186</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>90</td>\n",
       "      <td>186</td>\n",
       "      <td>174</td>\n",
       "      <td>186</td>\n",
       "      <td>186</td>\n",
       "      <td>195</td>\n",
       "      <td>186</td>\n",
       "      <td>195</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fold  train_start  train_end  last_window_start  last_window_end  \\\n",
       "0     0            0         96                 84               96   \n",
       "1     1           10        106                 94              106   \n",
       "2     2           20        116                104              116   \n",
       "3     3           30        126                114              126   \n",
       "4     4           40        136                124              136   \n",
       "5     5           50        146                134              146   \n",
       "6     6           60        156                144              156   \n",
       "7     7           70        166                154              166   \n",
       "8     8           80        176                164              176   \n",
       "9     9           90        186                174              186   \n",
       "\n",
       "   test_start  test_end  test_start_with_gap  test_end_with_gap  \\\n",
       "0          96       106                   96                106   \n",
       "1         106       116                  106                116   \n",
       "2         116       126                  116                126   \n",
       "3         126       136                  126                136   \n",
       "4         136       146                  136                146   \n",
       "5         146       156                  146                156   \n",
       "6         156       166                  156                166   \n",
       "7         166       176                  166                176   \n",
       "8         176       186                  176                186   \n",
       "9         186       195                  186                195   \n",
       "\n",
       "   fit_forecaster  \n",
       "0            True  \n",
       "1            True  \n",
       "2            True  \n",
       "3            True  \n",
       "4            True  \n",
       "5            True  \n",
       "6            True  \n",
       "7            True  \n",
       "8            True  \n",
       "9            True  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# TimeSeriesFold to visualize\n",
    "# ==============================================================================\n",
    "cv = TimeSeriesFold(\n",
    "         steps                 = 10,\n",
    "         initial_train_size    = 96,\n",
    "         fold_stride           = None,\n",
    "         window_size           = 12,\n",
    "         refit                 = True,\n",
    "         gap                   = 0,\n",
    "         allow_incomplete_fold = True,\n",
    "         verbose               = False\n",
    "     )\n",
    "\n",
    "cv.split(X=data, as_pandas=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "9dec1945",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Backtesting GIF creator\n",
    "# ==============================================================================\n",
    "\n",
    "# Reset matplotlib to default settings\n",
    "plt.rcdefaults()          # Restore all rcParams to default state\n",
    "plt.style.use('default')  # 'default' style\n",
    "\n",
    "backtesting_gif_creator(\n",
    "    data             = data['y'],\n",
    "    cv               = cv,\n",
    "    series_to_plot   = None,\n",
    "    plot_last_window = False,\n",
    "    filename         = \"backtesting.gif\",\n",
    "    figsize          = (9, 4),\n",
    "    plt_style        = 'ggplot',\n",
    "    title_template   = \"Backtesting — Fold {fold_num} (refit: {refit})\",\n",
    "    fps              = 2,\n",
    "    dpi              = 120\n",
    ");"
   ]
  }
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